Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: receiving, by a computer system, a hierarchical dataset of a treemap configured to be visualized in a data visualization, wherein the hierarchical dataset has a plurality of data attributes corresponding to columns of the hierarchical dataset and a plurality of data records corresponding to rows of the hierarchical dataset, wherein the data visualization has a plurality of hierarchical depth levels, each depth level being associated with a data attribute in the plurality of data attributes, and wherein each depth level has graphical elements being associated with one or more data records; determining, by the computer system, a plurality of aggregate functions, each of the plurality of aggregate functions determined based at least in part on a plurality of different graphical attributes of the graphical elements of the data visualization, the different graphical attributes identifying characteristics of the treemap; aggregating, by the computer system, data values from the one or more data records associated with the graphical elements of at least one depth level of the plurality of hierarchical depth levels, and wherein at least one aggregate function from the plurality of aggregate functions is used to perform the aggregating; generating aggregated data values for the at least one depth level based at least in part on the plurality of aggregate functions; and rendering, by the computer system, the data visualization based on the aggregated data values.
A computer system visualizes hierarchical data (like a treemap) by aggregating data at different levels of the hierarchy. The system receives a hierarchical dataset and determines a set of aggregate functions (sum, average, etc.). These aggregate functions are chosen based on the visual characteristics of the treemap (e.g., color, size). The system then applies these functions to data records at each level of the hierarchy to generate aggregated data values. Finally, the treemap is rendered, displaying the aggregated data. Each level in the treemap corresponds to a specific data attribute (column) of the dataset and displays graphical elements associated with data records (rows).
2. The method of claim 1 , wherein determining the plurality of aggregate functions includes receiving through a user interface a selection of at least one aggregate function.
The method for visualizing hierarchical data as described in claim 1, where determining the aggregate functions includes receiving a user's selection of at least one aggregate function through a user interface. This allows the user to explicitly choose which aggregation method (e.g., sum, average) is used for the data visualization, providing control over how data is summarized at each hierarchical level.
3. The method of claim 1 , wherein the determination of at least one aggregate function from the plurality of aggregate functions is based on selection intelligence.
The method for visualizing hierarchical data as described in claim 1, where the determination of at least one aggregate function is based on "selection intelligence". This means the system automatically selects appropriate aggregate functions based on certain criteria, rather than relying solely on user input.
4. The method of claim 3 , wherein the selection intelligence includes selecting an aggregate function based on inherent properties of a type of the data visualization.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" involves choosing an aggregate function based on the inherent properties of the type of data visualization. For example, if the visualization is inherently designed to show sums, the system automatically selects a summation aggregate function.
5. The method of claim 3 , wherein the selection intelligence includes selecting a summation aggregate function for a two-dimensional space-filling data visualization.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" involves selecting a summation aggregate function when the data visualization is a two-dimensional space-filling type, like a treemap. This ensures that the area of each rectangle in the treemap represents the sum of its underlying data.
6. The method of claim 3 , wherein the selection intelligence includes selecting an aggregate function having a metric which is the same as a metric used to determine data values of the hierarchical dataset.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" involves selecting an aggregate function that uses the same metric as the data values in the hierarchical dataset. For example, if the dataset contains sales figures in dollars, the system may choose an aggregate function that also produces dollar amounts (like summation or average).
7. The method of claim 3 , wherein the selection intelligence includes selecting an average aggregate function.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" includes selecting an average aggregate function. The system automatically decides that using an average will produce a useful visualization based on the data and visual characteristics.
8. The method of claim 3 , wherein the selection intelligence includes selecting a summation aggregation function.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" includes selecting a summation aggregation function. The system automatically decides that using summation will produce a useful visualization based on the data and visual characteristics.
9. The method of claim 3 , wherein the selection intelligence includes selecting an aggregation function based on a type of at least one graphical attribute of the data visualization.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" includes selecting an aggregation function based on the type of at least one graphical attribute of the data visualization. For instance, if a graphical attribute is color, the choice of aggregate function is affected by the nature of the color scale and what data it represents.
10. The method of claim 3 , wherein the selection intelligence includes selecting an average aggregation function, and wherein at least one graphical attribute of the data visualization is a color graphical attribute.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" includes selecting an average aggregation function, and at least one graphical attribute of the data visualization is a color. This could mean the color of elements in the visualization represents the average value of some data category.
11. The method of claim 1 , further comprising generating a data table for each hierarchical depth level, the data table including the aggregated data values for the hierarchical depth level.
The method for visualizing hierarchical data as described in claim 1, and further including the step of generating a data table for each hierarchical depth level. These tables would contain the aggregated data values that are used to generate the data visualization, providing a tabular view of the aggregated data at each level.
12. A non-transitory computer-readable medium having stored thereon program code executable by a computer system, the program code comprising: code for receiving a hierarchical dataset of a treemap configured to be visualized in a data visualization, wherein the hierarchical dataset has a plurality of data attributes corresponding to columns of the hierarchical dataset and a plurality of data records corresponding to rows of the hierarchical dataset, wherein the data visualization has a plurality of hierarchical depth levels, each depth level being associated with a data attribute in the plurality of data attributes, and wherein each depth level has graphical elements being associated with one or more data records; code for determining a plurality of aggregate functions, each of the plurality of aggregate functions determined based at least in part on a plurality of different graphical attributes of the graphical elements of the data visualization, the different graphical attributes identifying characteristics of the treemap; code for aggregating data values from the one or more data records associated with the graphical elements of at least one depth level of the plurality if hierarchical depth levels, and wherein at least one aggregate function from the plurality of aggregate functions is used to perform the aggregating; generating aggregated data values for the at least one depth level based at least in part on the plurality of aggregate functions; and code for rendering the data visualization based on the aggregated data values.
A computer-readable medium stores instructions that, when executed, cause a computer to visualize hierarchical data (like a treemap) by aggregating data at different levels. The instructions include: receiving a hierarchical dataset; determining a set of aggregate functions (sum, average, etc.) based on the visual characteristics of the treemap (e.g., color, size); applying these functions to data records at each level of the hierarchy to generate aggregated data values; and rendering the treemap, displaying the aggregated data. Each level in the treemap corresponds to a specific data attribute (column) of the dataset.
13. The non-transitory computer-readable medium according to claim 12 , wherein determining the plurality of aggregate functions includes receiving through a user interface a selection of at least one aggregate function.
The computer-readable medium for visualizing hierarchical data as described in claim 12, where determining the aggregate functions includes receiving a user's selection of at least one aggregate function through a user interface. This allows the user to explicitly choose which aggregation method (e.g., sum, average) is used for the data visualization.
14. The non-transitory computer-readable medium according to claim 12 , wherein the determination of at least one aggregate function from the plurality of aggregate functions is based on selection intelligence.
The computer-readable medium for visualizing hierarchical data as described in claim 12, where the determination of at least one aggregate function is based on "selection intelligence." This means the system automatically selects appropriate aggregate functions based on certain criteria, rather than relying solely on user input.
15. A system comprising: a processor; and a memory coupled to the processor, the memory configured to store a plurality of code modules which when executed by the processor cause the processor to: receive a hierarchical dataset of a treemap configured to be visualized in a data visualization, wherein the hierarchical dataset has a plurality of data attributes corresponding to columns of the hierarchical dataset and a plurality of data records corresponding to rows of the hierarchical dataset, wherein the data visualization has a plurality of hierarchical depth levels, each depth level being associated with a data attribute in the plurality of data attributes, and wherein each depth level has graphical elements being associated with one or more data records; determine a plurality of aggregate functions, each of the plurality of aggregate functions determined based at least in part on a plurality of different graphical attributes of the graphical elements of the data visualization, the different graphical attributes identifying characteristics of the treemap; aggregate data values from the one or more data records associated with the graphical elements of at least one depth level of the plurality of hierarchical depth levels, and wherein at least one aggregate function from the plurality of aggregate functions is used to perform the aggregating; generate aggregated data values for the at least one depth level based at least in part on the plurality of aggregate functions; and render the data visualization based on the aggregated data values.
A system visualizes hierarchical data (like a treemap) by aggregating data at different levels. The system includes a processor and memory storing instructions to: receive a hierarchical dataset; determine a set of aggregate functions (sum, average, etc.) based on the visual characteristics of the treemap (e.g., color, size); apply these functions to data records at each level of the hierarchy to generate aggregated data values; and render the treemap, displaying the aggregated data. Each level in the treemap corresponds to a specific data attribute (column) of the dataset.
16. The system of claim 15 , wherein determining the plurality of aggregate functions includes receiving through a user interface a selection of the at least one aggregate function.
The system for visualizing hierarchical data as described in claim 15, where determining the aggregate functions includes receiving a user's selection of at least one aggregate function through a user interface. This allows the user to explicitly choose which aggregation method (e.g., sum, average) is used for the data visualization.
17. The system of claim 15 , wherein the determination of at least one aggregate function from the plurality of aggregate functions is based on selection intelligence.
The system for visualizing hierarchical data as described in claim 15, where the determination of at least one aggregate function is based on "selection intelligence." This means the system automatically selects appropriate aggregate functions based on certain criteria, rather than relying solely on user input.
18. The method of claim 1 , wherein the data visualization includes at least three hierarchical depth levels.
The method for visualizing hierarchical data as described in claim 1, where the data visualization includes at least three hierarchical depth levels. This means the treemap has at least three levels of nested data, each representing a different category or attribute from the dataset.
19. The method of claim 1 , wherein at least one aggregate function of the plurality of aggregate functions is modified.
The method for visualizing hierarchical data as described in claim 1, where at least one aggregate function is modified. The user can adjust the formula, constants, or inputs to the aggregate function to tailor its behavior to specific data characteristics or visualization goals, beyond simply selecting a pre-defined function.
20. The method of claim 3 , wherein the selection intelligence includes selecting an aggregate function based on a user role.
The method for visualizing hierarchical data as described in claim 3, where the "selection intelligence" includes selecting an aggregate function based on a user role. For example, an executive might be presented with different aggregate function options than a data analyst, based on their typical data exploration needs. This customizes the aggregation and visualization process based on the user's function or permissions within the system.
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December 9, 2014
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